
"""simple docstring"""


from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
      from PIL import Image

      from ..image_utils import load_image

if is_tf_available():
      import tensorflow as tf

      from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
      from ..tf_utils import stable_softmax

if is_torch_available():
      from ..models.auto.modeling_auto import MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING

SCREAMING_SNAKE_CASE			  =  logging.get_logger(__name__)



@add_end_docstrings(_lowerCAmelCase						)
class 	__a	(   _lowerCAmelCase						):






       def __init__(	self			:				Any							,   *UpperCAmelCase_			:				Optional[int]							,   **UpperCAmelCase_			:				str      )->	Optional[int]:







              """simple docstring"""
              super().__init__(*UpperCAmelCase_							,   **UpperCAmelCase_      )
              requires_backends(self							,   "vision"      )
              self.check_model_type(
                  TF_MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING
                  if self.framework == "tf"
                  else MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]							,   UpperCAmelCase_			:				Tuple=None      )->	Tuple:







              """simple docstring"""
              UpperCamelCase       			=		{}
              if top_k is not None:
                     UpperCamelCase       			=		top_k
              return {}, {}, postprocess_params






       def __call__(	self			:				Optional[Any]							,   UpperCAmelCase_			:				Union[str, List[str], "Image.Image", List["Image.Image"]]							,   **UpperCAmelCase_			:				Tuple      )->	Dict:







              """simple docstring"""
              return super().__call__(UpperCAmelCase_							,   **UpperCAmelCase_      )






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[Any]							,   UpperCAmelCase_			:				Dict      )->	Optional[Any]:







              """simple docstring"""
              UpperCamelCase       			=		load_image(UpperCAmelCase_      )
              UpperCamelCase       			=		self.image_processor(images=UpperCAmelCase_							,   return_tensors=self.framework      )
              return model_inputs






       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]							,   UpperCAmelCase_			:				Optional[Any]      )->	int:







              """simple docstring"""
              UpperCamelCase       			=		self.model(**UpperCAmelCase_      )
              return model_outputs






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]							,   UpperCAmelCase_			:				Any							,   UpperCAmelCase_			:				Optional[Any]=5      )->	Tuple:







              """simple docstring"""
              if top_k > self.model.config.num_labels:
                     UpperCamelCase       			=		self.model.config.num_labels

              if self.framework == "pt":
                     UpperCamelCase       			=		model_outputs.logits.softmax(-1      )[0]
                     UpperCamelCase			,							UpperCamelCase       			=		probs.topk(UpperCAmelCase_      )
              elif self.framework == "tf":
                     UpperCamelCase       			=		stable_softmax(model_outputs.logits							,   axis=-1      )[0]
                     UpperCamelCase       			=		tf.math.top_k(UpperCAmelCase_							,   k=UpperCAmelCase_      )
                     UpperCamelCase			,							UpperCamelCase       			=		topk.values.numpy(), topk.indices.numpy()
              else:
                     raise ValueError(f"Unsupported framework: {self.framework}"      )

              UpperCamelCase       			=		scores.tolist()
              UpperCamelCase       			=		ids.tolist()
              return [{"score": score, "label": self.model.config.idalabel[_id]} for score, _id in zip(UpperCAmelCase_							,   UpperCAmelCase_      )]







"""simple docstring"""


import argparse
import ast
import logging
import os
import sys

import pandas as pd
import torch
from tqdm import tqdm

from transformers import BartForConditionalGeneration, RagRetriever, RagSequenceForGeneration, RagTokenForGeneration
from transformers import logging as transformers_logging


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # isort:skip
from utils_rag import exact_match_score, fa_score  # noqa: E402 # isort:skip


SCREAMING_SNAKE_CASE			  =  logging.getLogger(__name__)
logging.basicConfig(level=logging.INFO)

transformers_logging.set_verbosity_info()



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				Tuple:







       """simple docstring"""
       if "token" in model_name_or_path:
              return "rag_token"
       if "sequence" in model_name_or_path:
              return "rag_sequence"
       if "bart" in model_name_or_path:
              return "bart"
       return None



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				List[str]:







       """simple docstring"""
       return max(metric_fn(UpperCAmelCase_		,   UpperCAmelCase_					) for gt in ground_truths					)



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				List[Any]:







       """simple docstring"""
       UpperCamelCase       			=		[line.strip() for line in open(UpperCAmelCase_		,   "r"					).readlines()]
       UpperCamelCase       			=		[]

       if args.gold_data_mode == "qa":
              UpperCamelCase       			=		pd.read_csv(UpperCAmelCase_		,   sep="\t"		,   header=UpperCAmelCase_					)
              for answer_list in data[1]:
                     UpperCamelCase       			=		ast.literal_eval(UpperCAmelCase_					)
                     answers.append(UpperCAmelCase_					)
       else:
              UpperCamelCase       			=		[line.strip() for line in open(UpperCAmelCase_		,   "r"					).readlines()]
              UpperCamelCase       			=		[[reference] for reference in references]

       UpperCamelCase       			=		UpperCamelCase       			=		UpperCamelCase       			=		0
       for prediction, ground_truths in zip(UpperCAmelCase_		,   UpperCAmelCase_					):
              total += 1
              em += metric_max_over_ground_truths(UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)
              fa += metric_max_over_ground_truths(UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)

       UpperCamelCase       			=		100.0 * em / total
       UpperCamelCase       			=		100.0 * fa / total

       logger.info(F"F1: {fa:.2f}"					)
       logger.info(F"EM: {em:.2f}"					)



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				List[Any]:







       """simple docstring"""
       UpperCamelCase       			=		args.k
       UpperCamelCase       			=		[line.strip() for line in open(UpperCAmelCase_		,   "r"					).readlines()]
       UpperCamelCase       			=		[line.strip() for line in open(UpperCAmelCase_		,   "r"					).readlines()]

       UpperCamelCase       			=		UpperCamelCase       			=		0
       for hypo, reference in zip(UpperCAmelCase_		,   UpperCAmelCase_					):
              UpperCamelCase       			=		set(hypo.split("\t"					)[:k]					)
              UpperCamelCase       			=		set(reference.split("\t"					)					)
              total += 1
              em += len(hypo_provenance & ref_provenance					) / k

       UpperCamelCase       			=		100.0 * em / total
       logger.info(F"Precision@{k}: {em: .2f}"					)



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				Optional[int]:







       """simple docstring"""
       def strip_title(UpperCAmelCase_					):
              if title.startswith("\""					):
                     UpperCamelCase       			=		title[1:]
              if title.endswith("\""					):
                     UpperCamelCase       			=		title[:-1]
              return title

       UpperCamelCase       			=		rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
           UpperCAmelCase_		,   return_tensors="pt"		,   padding=UpperCAmelCase_		,   truncation=UpperCAmelCase_		,   )["input_ids"].to(args.device					)

       UpperCamelCase       			=		rag_model.rag.question_encoder(UpperCAmelCase_					)
       UpperCamelCase       			=		question_enc_outputs[0]

       UpperCamelCase       			=		rag_model.retriever(
           UpperCAmelCase_		,   question_enc_pool_output.cpu().detach().to(torch.floataa					).numpy()		,   prefix=rag_model.rag.generator.config.prefix		,   n_docs=rag_model.config.n_docs		,   return_tensors="pt"		,   )
       UpperCamelCase       			=		rag_model.retriever.index.get_doc_dicts(result.doc_ids					)
       UpperCamelCase       			=		[]
       for docs in all_docs:
              UpperCamelCase       			=		[strip_title(UpperCAmelCase_					) for title in docs["title"]]
              provenance_strings.append("\t".join(UpperCAmelCase_					)					)
       return provenance_strings



def 						lowerCamelCase__      (				UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)->				Optional[int]:







       """simple docstring"""
       with torch.no_grad():
              UpperCamelCase       			=		rag_model.retriever.question_encoder_tokenizer.batch_encode_plus(
                  UpperCAmelCase_		,   return_tensors="pt"		,   padding=UpperCAmelCase_		,   truncation=UpperCAmelCase_					)

              UpperCamelCase       			=		inputs_dict.input_ids.to(args.device					)
              UpperCamelCase       			=		inputs_dict.attention_mask.to(args.device					)
              UpperCamelCase       			=		rag_model.generate(  # rag_model overwrites generate
                  UpperCAmelCase_		,   attention_mask=UpperCAmelCase_		,   num_beams=args.num_beams		,   min_length=args.min_length		,   max_length=args.max_length		,   early_stopping=UpperCAmelCase_		,   num_return_sequences=1		,   bad_words_ids=[[0, 0]]		,   )
              UpperCamelCase       			=		rag_model.retriever.generator_tokenizer.batch_decode(UpperCAmelCase_		,   skip_special_tokens=UpperCAmelCase_					)

              if args.print_predictions:
                     for q, a in zip(UpperCAmelCase_		,   UpperCAmelCase_					):
                            logger.info("Q: {} - A: {}".format(UpperCAmelCase_		,   UpperCAmelCase_					)					)

              return answers



def 						lowerCamelCase__      (				)->				Any:







       """simple docstring"""
       UpperCamelCase       			=		argparse.ArgumentParser()
       parser.add_argument(
           "--model_type"		,   choices=["rag_sequence", "rag_token", "bart"]		,   type=UpperCAmelCase_		,   help=(
               "RAG model type: rag_sequence, rag_token or bart, if none specified, the type is inferred from the"
               " model_name_or_path"
           )		,   )
       parser.add_argument(
           "--index_name"		,   default=UpperCAmelCase_		,   choices=["exact", "compressed", "legacy"]		,   type=UpperCAmelCase_		,   help="RAG model retriever type"		,   )
       parser.add_argument(
           "--index_path"		,   default=UpperCAmelCase_		,   type=UpperCAmelCase_		,   help="Path to the retrieval index"		,   )
       parser.add_argument("--n_docs"		,   default=5		,   type=UpperCAmelCase_		,   help="Number of retrieved docs"					)
       parser.add_argument(
           "--model_name_or_path"		,   default=UpperCAmelCase_		,   type=UpperCAmelCase_		,   required=UpperCAmelCase_		,   help="Path to pretrained checkpoints or model identifier from huggingface.co/models"		,   )
       parser.add_argument(
           "--eval_mode"		,   choices=["e2e", "retrieval"]		,   default="e2e"		,   type=UpperCAmelCase_		,   help=(
               "Evaluation mode, e2e calculates exact match and F1 of the downstream task, retrieval calculates"
               " precision@k."
           )		,   )
       parser.add_argument("--k"		,   default=1		,   type=UpperCAmelCase_		,   help="k for the precision@k calculation"					)
       parser.add_argument(
           "--evaluation_set"		,   default=UpperCAmelCase_		,   type=UpperCAmelCase_		,   required=UpperCAmelCase_		,   help="Path to a file containing evaluation samples"		,   )
       parser.add_argument(
           "--gold_data_path"		,   default=UpperCAmelCase_		,   type=UpperCAmelCase_		,   required=UpperCAmelCase_		,   help="Path to a tab-separated file with gold samples"		,   )
       parser.add_argument(
           "--gold_data_mode"		,   default="qa"		,   type=UpperCAmelCase_		,   choices=["qa", "ans"]		,   help=(
               "Format of the gold data file"
               "qa - a single line in the following format: question [tab] answer_list"
               "ans - a single line of the gold file contains the expected answer string"
           )		,   )
       parser.add_argument(
           "--predictions_path"		,   type=UpperCAmelCase_		,   default="predictions.txt"		,   help="Name of the predictions file, to be stored in the checkpoints directory"		,   )
       parser.add_argument(
           "--eval_all_checkpoints"		,   action="store_true"		,   help="Evaluate all checkpoints starting with the same prefix as model_name ending and ending with step number"		,   )
       parser.add_argument(
           "--eval_batch_size"		,   default=8		,   type=UpperCAmelCase_		,   help="Batch size per GPU/CPU for evaluation."		,   )
       parser.add_argument(
           "--recalculate"		,   help="Recalculate predictions even if the prediction file exists"		,   action="store_true"		,   )
       parser.add_argument(
           "--num_beams"		,   default=4		,   type=UpperCAmelCase_		,   help="Number of beams to be used when generating answers"		,   )
       parser.add_argument("--min_length"		,   default=1		,   type=UpperCAmelCase_		,   help="Min length of the generated answers"					)
       parser.add_argument("--max_length"		,   default=50		,   type=UpperCAmelCase_		,   help="Max length of the generated answers"					)

       parser.add_argument(
           "--print_predictions"		,   action="store_true"		,   help="If True, prints predictions while evaluating."		,   )
       parser.add_argument(
           "--print_docs"		,   action="store_true"		,   help="If True, prints docs retried while generating."		,   )
       UpperCamelCase       			=		parser.parse_args()
       UpperCamelCase       			=		torch.device("cuda" if torch.cuda.is_available() else "cpu"					)
       return args



def 						lowerCamelCase__      (				UpperCAmelCase_					)->				List[Any]:







       """simple docstring"""
       UpperCamelCase       			=		{}
       if args.model_type is None:
              UpperCamelCase       			=		infer_model_type(args.model_name_or_path					)
              assert args.model_type is not None
       if args.model_type.startswith("rag"					):
              UpperCamelCase       			=		RagTokenForGeneration if args.model_type == "rag_token" else RagSequenceForGeneration
              UpperCamelCase       			=		args.n_docs
              if args.index_name is not None:
                     UpperCamelCase       			=		args.index_name
              if args.index_path is not None:
                     UpperCamelCase       			=		args.index_path
       else:
              UpperCamelCase       			=		BartForConditionalGeneration

       UpperCamelCase       			=		(
           [f.path for f in os.scandir(args.model_name_or_path					) if f.is_dir()]
           if args.eval_all_checkpoints
           else [args.model_name_or_path]
       )

       logger.info("Evaluate the following checkpoints: %s"		,   UpperCAmelCase_					)

       UpperCamelCase       			=		get_scores if args.eval_mode == "e2e" else get_precision_at_k
       UpperCamelCase       			=		evaluate_batch_eae if args.eval_mode == "e2e" else evaluate_batch_retrieval

       for checkpoint in checkpoints:
              if os.path.exists(args.predictions_path					) and (not args.recalculate):
                     logger.info("Calculating metrics based on an existing predictions file: {}".format(args.predictions_path					)					)
                     score_fn(UpperCAmelCase_		,   args.predictions_path		,   args.gold_data_path					)
                     continue

              logger.info("***** Running evaluation for {} *****".format(UpperCAmelCase_					)					)
              logger.info("  Batch size = %d"		,   args.eval_batch_size					)
              logger.info("  Predictions will be stored under {}".format(args.predictions_path					)					)

              if args.model_type.startswith("rag"					):
                     UpperCamelCase       			=		RagRetriever.from_pretrained(UpperCAmelCase_		,   **UpperCAmelCase_					)
                     UpperCamelCase       			=		model_class.from_pretrained(UpperCAmelCase_		,   retriever=UpperCAmelCase_		,   **UpperCAmelCase_					)
                     model.retriever.init_retrieval()
              else:
                     UpperCamelCase       			=		model_class.from_pretrained(UpperCAmelCase_		,   **UpperCAmelCase_					)
              model.to(args.device					)

              with open(args.evaluation_set		,   "r"					) as eval_file, open(args.predictions_path		,   "w"					) as preds_file:
                     UpperCamelCase       			=		[]
                     for line in tqdm(UpperCAmelCase_					):
                            questions.append(line.strip()					)
                            if len(UpperCAmelCase_					) == args.eval_batch_size:
                                   UpperCamelCase       			=		evaluate_batch_fn(UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)
                                   preds_file.write("\n".join(UpperCAmelCase_					) + "\n"					)
                                   preds_file.flush()
                                   UpperCamelCase       			=		[]
                     if len(UpperCAmelCase_					) > 0:
                            UpperCamelCase       			=		evaluate_batch_fn(UpperCAmelCase_		,   UpperCAmelCase_		,   UpperCAmelCase_					)
                            preds_file.write("\n".join(UpperCAmelCase_					)					)
                            preds_file.flush()

                     score_fn(UpperCAmelCase_		,   args.predictions_path		,   args.gold_data_path					)


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE			  =  get_args()
      main(args)





